Publications (74)
Competency-Aware Neural Machine Translation: Can Machine Translation Know its Own Translation Quality?
Pei Zhang, Baosong Yang, Haoran Wei +4
Neural machine translation (NMT) is often criticized for failures that happen without awareness. The lack of competency awareness makes NMT untrustworthy. This is in sharp contrast…
MELM: Data Augmentation with Masked Entity Language Modeling for Low-Resource NER
Ran Zhou, Xin Li, Ruidan He +4
Data augmentation is an effective solution to data scarcity in low-resource scenarios. However, when applied to token-level tasks such as NER, data augmentation methods often suffe…
Distinguish Before Answer: Generating Contrastive Explanation as Knowledge for Commonsense Question Answering
Qianglong Chen, Guohai Xu, Ming Yan +4
Existing knowledge-enhanced methods have achieved remarkable results in certain QA tasks via obtaining diverse knowledge from different knowledge bases. However, limited by the pro…
SPACE-3: Unified Dialog Model Pre-training for Task-Oriented Dialog Understanding and Generation
Wanwei He, Yinpei Dai, Min Yang +4
Recently, pre-training methods have shown remarkable success in task-oriented dialog (TOD) systems. However, most existing pre-trained models for TOD focus on either dialog underst…
DAGA: Data Augmentation with a Generation Approach for Low-resource Tagging Tasks
Bosheng Ding, Linlin Liu, Lidong Bing +5
Data augmentation techniques have been widely used to improve machine learning performance as they enhance the generalization capability of models. In this work, to generate high q…
A Deep Cascade Model for Multi-Document Reading Comprehension
Ming Yan, Jiangnan Xia, Chen Wu +7
A fundamental trade-off between effectiveness and efficiency needs to be balanced when designing an online question answering system. Effectiveness comes from sophisticated functio…